Rationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear.Objectives: This study aimed to determine whether a low-oxygenation... Show moreRationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear.Objectives: This study aimed to determine whether a low-oxygenation strategy would lower 28-day mortality compared with a high-oxygenation strategy.Methods: This randomized multicenter trial included mechanically ventilated ICU patients with an expected ventilation duration of at least 24 hours. Patients were randomized 1:1 to a low-oxygenation (Pa-O2, 55-80mmHg; or oxygen saturation as measured by pulse oximetry, 91-94%) or high-oxygenation (Pa-O2, 110-150mmHg; or oxygen saturation as measured by pulse oximetry, 96-100%) target until ICU discharge or 28 days after randomization, whichever came first. The primary outcome was 28-day mortality. The study was stopped prematurely because of the COVID-19 pandemic when 664 of the planned 1,512 patients were included.Measurements and Main Results: Between November 2018 and November 2021, a total of 664 patients were included in the trial: 335 in the low-oxygenation group and 329 in the high-oxygenation group. The median achieved Pa-O2 was 75mmHg (interquartile range, 70-84) and 115mmHg (interquartile range, 100-129) in the low- and high-oxygenation groups, respectively. At Day 28, 129 (38.5%) and 114 (34.7%) patients had died in the low- and high-oxygenation groups, respectively (risk ratio, 1.11; 95% confidence interval, 0.9-1.4; P = 0.30). At least one serious adverse event was reported in 12 (3.6%) and 17 (5.2%) patients in the low- and high-oxygenation groups, respectively.Conclusions: Among mechanically ventilated ICU patients with an expected mechanical ventilation duration of at least 24 hours, using a low-oxygenation strategy did not result in a reduction of 28-day mortality compared with a high-oxygenation strategy. Show less
Jonge, E. de; Vooren, M. van der; Gillis, J.M.E.P.; Prado, M.R. del; Wigbers, J.; Bakhshi-Raiez, F.; Kraemer, C.V.E. 2022
Background: Supplementation of calcium during continuous venovenous hemofiltration (CVVH) with citrate anticoagulation is usually titrated using a target blood ionized calcium concentration. Plasma... Show moreBackground: Supplementation of calcium during continuous venovenous hemofiltration (CVVH) with citrate anticoagulation is usually titrated using a target blood ionized calcium concentration. Plasma calcium concentrations may be normal despite substantial calcium loss, by mobilization of calcium from the skeleton. Aim of our study is to develop an equation to calculate CVVH calcium and to retrospectively calculate CVVH calcium balance in a cohort of ICU-patients. Methods: This is a single-center retrospective observational cohort study. In a subcohort of patients, all calcium excretion measurements in patients treated with citrate CVVH were randomly divided into a development set (n = 324 in 42 patients) and a validation set (n = 441 in 42 different patients). Using mixed linear models, we developed an equation to calculate calcium excretion from routinely available parameters. We retrospectively calculated calcium balance in 788 patients treated with citrate CVVH between 2014 and 2021. Results: Calcium excretion (mmol/24 h) was - 1.2877 + 0.646*[Ca](blood,total) * ultrafiltrate (l/24 h) + 0.107*blood flow (ml/h). The mean error of the estimation was - 1.0 +/- 6.7 mmol/24 h, the mean absolute error was 4.8 +/- 4.8 mmol/24 h. Calculated calcium excretion was 105.8 +/- 19.3 mmol/24 h. Mean daily CVVH calcium balance was - 12.0 +/- 20.0 mmol/24 h. Mean cumulative calcium balance ranged from - 3687 to 448 mmol. Conclusion: During citrate CVVH, calcium balance was negative in most patients, despite supplementation of calcium based on plasma ionized calcium levels. This may contribute to demineralization of the skeleton. We propose that calcium supplementation should be based on both plasma ionized calcium and a simple calculation of calcium excretion by CVVH.[GRAPHICS]. Show less
Purpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs. Materials and methods: Data from the National-Intensive... Show morePurpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs. Materials and methods: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared. Results: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58. Conclusion: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Show less
Background To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands. Methods Data from the National Intensive Care Evaluation (NICE)... Show moreBackground To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands. Methods Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020-January 31, 2021) and the final upsurge of the second wave, called the 'third wave' (N = 4602: February 1, 2021-June 30, 2021) were compared with those during the first wave (N = 2733, February-May 24, 2020). Results During the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71-0.90]). Conclusions These data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19. Show less
Schoe, A.; Bakhshi-Raiez, F.; Keizer, N. de; Dissel, J.T. van; Jonge, E. de 2020
Background There are many prognostic models and scoring systems in use to predict mortality in ICU patients. The only general ICU scoring system developed and validated for patients after cardiac... Show moreBackground There are many prognostic models and scoring systems in use to predict mortality in ICU patients. The only general ICU scoring system developed and validated for patients after cardiac surgery is the APACHE-IV model. This is, however, a labor-intensive scoring system requiring a lot of data and could therefore be prone to error. The SOFA score on the other hand is a simpler system, has been widely used in ICUs and could be a good alternative. The goal of the study was to compare the SOFA score with the APACHE-IV and other ICU prediction models. Methods We investigated, in a large cohort of cardiac surgery patients admitted to Dutch ICUs, how well the SOFA score from the first 24 h after admission, predict hospital and ICU mortality in comparison with other recalibrated general ICU scoring systems. Measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve (AUC), Brier score, R-2, C-statistic) were calculated using bootstrapping. The cohort consisted of 36,632 Patients from the Dutch National Intensive Care Evaluation (NICE) registry having had a cardiac surgery procedure for which ICU admission was necessary between January 1st, 2006 and June 31st, 2018. Results Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM24-II - models to predict hospital mortality was good with an AUC of respectively: 0.809, 0.851, 0.830, 0.850, 0.801. Discrimination of the SOFA-, APACHE-IV-, APACHE-II-, SAPS-II-, MPM24-II - models to predict ICU mortality was slightly better with AUCs of respectively: 0.809, 0.906, 0.892, 0.919, 0.862. Calibration of the models was generally poor. Conclusion Although the SOFA score had a good discriminatory power for hospital- and ICU mortality the discriminatory power of the APACHE-IV and SAPS-II was better. The SOFA score should not be preferred as mortality prediction model above traditional prognostic ICU-models. Show less
Objectives: To compare methods to adjust for confounding by disease severity during multicenter intervention studies in ICU, when different disease severity measures are collected across centers.... Show moreObjectives: To compare methods to adjust for confounding by disease severity during multicenter intervention studies in ICU, when different disease severity measures are collected across centers. Design: In silico simulation study using national registry data. Setting: Twenty mixed ICUs in The Netherlands. Subjects: Fifty-five-thousand six-hundred fifty-five ICU admissions between January 1, 2011, and January 1, 2016. Interventions: None. Measurements and Main Results: To mimic an intervention study with confounding, a fictitious treatment variable was simulated whose effect on the outcome was confounded by Acute Physiology and Chronic Health Evaluation IV predicted mortality (a common measure for disease severity). Diverse, realistic scenarios were investigated where the availability of disease severity measures (i.e., Acute Physiology and Chronic Health Evaluation IV, Acute Physiology and Chronic Health Evaluation II, and Simplified Acute Physiology Score II scores) varied across centers. For each scenario, eight different methods to adjust for confounding were used to obtain an estimate of the (fictitious) treatment effect. These were compared in terms of relative (%) and absolute (odds ratio) bias to a reference scenario where the treatment effect was estimated following correction for the Acute Physiology and Chronic Health Evaluation IV scores from all centers. Complete neglect of differences in disease severity measures across centers resulted in bias ranging from 10.2% to 173.6% across scenarios, and no commonly used methodology-such as two-stage modeling or score standardization-was able to effectively eliminate bias. In scenarios where some of the included centers had (only) Acute Physiology and Chronic Health Evaluation II or Simplified Acute Physiology Score II available (and not Acute Physiology and Chronic Health Evaluation IV), either restriction of the analysis to Acute Physiology and Chronic Health Evaluation IV centers alone or multiple imputation of Acute Physiology and Chronic Health Evaluation IV scores resulted in the least amount of relative bias (0.0% and 5.1% for Acute Physiology and Chronic Health Evaluation II, respectively, and 0.0% and 4.6% for Simplified Acute Physiology Score II, respectively). In scenarios where some centers used Acute Physiology and Chronic Health Evaluation II, regression calibration yielded low relative bias too (relative bias, 12.4%); this was not true if these same centers only had Simplified Acute Physiology Score II available (relative bias, 54.8%). Conclusions: When different disease severity measures are available across centers, the performance of various methods to control for confounding by disease severity may show important differences. When planning multicenter studies, researchers should make contingency plans to limit the use of or properly incorporate different disease measures across centers in the statistical analysis. Show less
Huson, M.A.; Bakhshi-Raiez, F.; Grobusch, M.P.; Jonge, E. de; Keizer, N.F. de; Poll, T. van der 2016
BACKGROUND\nPostoperative care for major elective cancer surgery is frequently provided on the Intensive Care Unit (ICU).\nOBJECTIVE\nTo analyze the characteristics and outcome of patients after... Show moreBACKGROUND\nPostoperative care for major elective cancer surgery is frequently provided on the Intensive Care Unit (ICU).\nOBJECTIVE\nTo analyze the characteristics and outcome of patients after ICU admission following elective surgery for different cancer diagnoses.\nMETHODS\nWe analyzed all ICU admissions following elective cancer surgery in the Netherlands collected in the National Intensive Care Evaluation registry between January 2007 and January 2012.\nRESULTS\n28,973 patients (9.0% of all ICU admissions; 40% female) were admitted to the ICU after elective cancer surgery. Of these admissions 77% were planned; in 23% of cases the decision for ICU admission was made during or directly after surgery. The most frequent malignancies were colorectal cancer (25.6%), lung cancer (18.5%) and tumors of the central nervous system (14.3%). Mechanical ventilation was necessary in 24.8% of all patients, most frequently after surgery for esophageal (62.5%) and head and neck cancer (50.2%); 20.7% of patients were treated with vasopressors in the acute postoperative phase, in particular after surgery for esophageal cancer (41.8%). The median length of stay on the ICU was 0.9 days (interquartile ranges [IQR] 0.8-1.5); surgery for esophageal cancer was associated with the longest ICU length of stay (median 2.0 days) with the largest variation (IQR 1.0-4.8 days). ICU mortality was 1.4%; surgery for gastrointestinal cancer was associated with the highest ICU mortality (colorectal cancer 2.2%, pancreatico-cholangiocarcinoma 2.0%).\nCONCLUSION\nElective cancer surgery represents a significant part of all ICU admissions, with a short length of stay and low mortality. Show less
Brinkman, S.; Bakhshi-Raiez, F.; Abu-Hanna, A.; Jonge, E. de; Keizer, N.F. de 2013
OBJECTIVE\nTo provide a generic approach for developing a domain-specific interface terminology on SNOMED CT and to apply this approach to the domain of intensive care.\nMETHODS\nThe process of... Show moreOBJECTIVE\nTo provide a generic approach for developing a domain-specific interface terminology on SNOMED CT and to apply this approach to the domain of intensive care.\nMETHODS\nThe process of developing an interface terminology on SNOMED CT can be regarded as six sequential phases: domain analysis, mapping from the domain concepts to SNOMED CT concepts, creating the SNOMED CT subset guided by the mapping, extending the subset with non-covered concepts, constraining the subset by removing irrelevant content, and deploying the subset in a terminology server.\nRESULTS\nThe APACHE IV classification, a standard in the intensive care with 445 diagnostic categories, served as the starting point for designing the interface terminology. The majority (89.2%) of the diagnostic categories from APACHE IV could be mapped to SNOMED CT concepts and for the remaining concepts a partial match was identified. The resulting initial set of mapped concepts consisted of 404 SNOMED CT concepts. This set could be extended to 83,125 concepts if all taxonomic children of these concepts were included. Also including all concepts that are referred to in the definition of other concepts lead to a subset of 233,782 concepts. An evaluation of the interface terminology should reveal what level of detail in the subset is suitable for the intensive care domain and whether parts need further constraining. In the final phase, the interface terminology is implemented in the intensive care in a locally developed terminology server to collect the reasons for intensive care admission.\nCONCLUSIONS\nWe provide a structure for the process of identifying a domain-specific interface terminology on SNOMED CT. We use this approach to design an interface terminology on SNOMED CT for the intensive care domain. This work is of value for other researchers who intend to build a domain-specific interface terminology on SNOMED CT. Show less
Bakhshi-Raiez, F.; Ahmadian, L.; Cornet, R.; Jonge, E. de; Keizer, N.F. de 2010
